An experimental analysis of situation awareness for cockpit display interface evaluation based on flight simulation

Aircraft cockpit display interface （CDI） is one of the most important human-machine interfaces for information perceiving. During the process of aircraft design, situation awareness （SA） is frequently considered to improve the design, as the CDI must provide enough SA for the pilot to maintain the flight safety. In order to study the SA in the pilot-aircraft system, a cockpit flight simulation environment is built up, which includes a virtual instrument panel, a flight visual display and the corresponding control system. Based on the simulation environment, a human-in-the-loop experiment is designed to measure the SA by the situation awareness global assessment technique （SAGAT）. Through the experiment, the SA degrees and heart rate （HR） data of the subjects are obtained, and the SA levels under different CDI designs are analyzed. The results show that analyzing the SA can serve as an objective way to evaluate the design of CDI, which could be proved from the consistent HR data. With this method, evaluations of the CDI design are performed in the experimental flight simulation environment, and optimizations could be guided through the analysis.

A model for discrimination and prediction of mental workload of aircraft cockpit display interface

With respect to the ergonomic evaluation and optimization in the mental task design of the aircraft cockpit display interface, the experimental measurement and theoretical modeling of mental workload were carried out under flight simulation task conditions using the performance evaluation, subjective evaluation and physiological measurement methods. The experimental results show that with an increased mental workload, the detection accuracy of flight operation significantly reduced and the reaction time was significantly prolonged; the standard deviation of R-R intervals（SDNN） significantly decreased, while the mean heart rate exhibited little change; the score of NASA_TLX scale significantly increased. On this basis, the indexes sensitive to mental workload were screened, and an integrated model for the discrimination and prediction of mental workload of aircraft cockpit display interface was established based on the Bayesian Fisher discrimination and classification method. The original validation and cross-validation methods were employed to test the accuracy of the results of discrimination and prediction of the integrated model, and the average prediction accuracies determined by these two methods are both higher than 85%. Meanwhile, the integrated model shows a higher accuracy in discrimination and prediction of mental workload compared with single indexes. The model proposed in this paper exhibits a satisfactory coincidence with the measured data and could accurately reflect the variation characteristics of the mental workload of aircraft cockpit display interface, thus providing a basis for the ergonomic evaluation and optimization design of the aircraft cockpit display interface in the future.